Image adaptive feature extraction method and application thereof

Patent metadata

Field	Value
Publication number	US-11295160-B2
Application number	US-201916676453-A
Country	US
Kind code	B2
Filing date	Nov 7, 2019
Priority date	Nov 19, 2018
Publication date	Apr 5, 2022
Grant date	Apr 5, 2022

Abstract

An image adaptive feature extraction method includes dividing an image into a plurality of blocks, performing a feature extraction processing on the plurality of blocks, and obtaining a block feature from each of the plurality of blocks after the feature extraction processing; calculating each block feature by means of a support vector machine (SVM) classifier, wherein each block feature is calculated to obtain a hyperplane normal vector; setting a threshold value, determining the block feature according to the hyperplane normal vector, recording the block as an adaptive feature block when a value of the hyperplane normal vector is higher than the threshold value, and integrating each adaptive feature block to form an adaptive feature image. Because an image adaptive feature extraction process is performed before a pedestrian image detection is calculated, and effective feature data is then selected, computational efficiency is boosted and detection pedestrian error probability is reduced.

First claim

What is claimed is:

1. An image adaptive feature extraction method, comprising steps of:
(A) dividing an image into a plurality of blocks, performing a feature extraction processing on the plurality of blocks, and obtaining a block feature from each of the plurality of blocks after the feature extraction processing, wherein the feature extraction processing involves more than one of a histogram of oriented gradients, a local binary pattern, and a histogram of local intensity difference;
(B) calculating each block feature by means of a support vector machine classifier, wherein each block feature is calculated to obtain a hyperplane normal vector; and
(C) setting a threshold value, determining the block feature according to the hyperplane normal vector, recording the block as an adaptive feature block when a value of the hyperplane normal vector is higher than the threshold value, and integrating each adaptive feature block to form an adaptive feature image.

2. A pedestrian thermal image detection method, comprising steps of:
(A) reading a raw thermal image, wherein the raw thermal image comprises a specific ambient information;
(B) dividing the raw thermal image into a plurality of blocks, performing a pedestrian feature extraction processing on the plurality of blocks, and obtaining a block feature from each of the plurality of blocks after the pedestrian feature extraction processing, wherein the pedestrian feature extraction processing involves more than one of a histogram of oriented gradients, a local binary pattern, and a histogram of local intensity difference;
(C) calculating each block feature by means of a support vector machine classifier, wherein each block feature is calculated to obtain a hyperplane normal vector;
(D) setting a threshold value, determining the block feature according to the hyperplane normal vector, recording the block as an adaptive feature block when a value of the hyperplane normal vector is higher than the threshold value, and integrating each adaptive feature block to form an pedestrian feature image; and
(E) performing a pedestrian image detection by means of the pedestrian feature image.

3. The pedestrian thermal image detection method according to claim 2 , wherein the pedestrian image detection is the histogram of oriented gradients.

4. The pedestrian thermal image detection method according to claim 2 , wherein the pedestrian image detection is the local binary pattern.

5. The pedestrian thermal image detection method according to claim 2 , wherein the pedestrian image detection is the histogram of local intensity difference.

6. The pedestrian thermal image detection method according to claim 2 , wherein the specific ambient information comprises a pedestrian image.

7. The pedestrian thermal image detection method according to claim 2 , wherein the support vector machine classifier is trained by using static humanoid sample data as a training sample database.

8. The pedestrian thermal image detection method according to claim 2 , wherein the support vector machine classifier is trained by using a probe-six dataset as a test database.

Assignees

• Nat Chung Shan Inst Science & Tech

Inventors

• Huang Shih-Shinh
• Chien Shih-Che
• Chang Feng-Chia
• Hsiao Yu-Sung
• Hsiao Chien-Hao

Classifications

• G06T7/73 Primary

  using feature-based methods · CPC title

• G06V20/00

  Scenes; Scene-specific elements (control of digital cameras H04N23/60) · CPC title

• G06V10/143

  Sensing or illuminating at different wavelengths · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V10/50

  by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis · CPC title